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NEW QUESTION # 101
Which of the following data model are included In the Splunk Common Information Model (CIM) add-on?
(select all that apply)
- A. Email
- B. Alerts
- C. User permissions
- D. Database
Answer: A,B,D
Explanation:
Reference:https://docs.splunk.com/Documentation/CIM/4.15.0/User/Overview
The Splunk Common Information Model (CIM) add-on is a collection of pre-built data models and knowledge
objects that help you normalize your data from different sources and make it easier to analyze and report on
it3. The CIM add-on includes several data models that cover various domains such as Alerts, Email, Database,
Network Traffic, Web and more3. Therefore, options A, B and C are correct because they are names of some
of the data models included in the CIM add-on. Option D is incorrect because User permissions is not a name
of a data model in the CIM add-on.
NEW QUESTION # 102
Calculated fields can be based on which of the following?
- A. Fields generated from a search string
- B. Tags
- C. Extracted fields
- D. Output fields for a lookup
Answer: C
Explanation:
Reference:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/definecalcfields
NEW QUESTION # 103
Which of the following objects can a calculated field use as a source?
- A. The eventtype field.
- B. A field added by an automatic lookup.
- C. An alias of a field.
- D. The tag field.
Answer: B
Explanation:
The correct answer is B. A field added by an automatic lookup.
A calculated field is a field that is added to events at search time by using an eval expression. A calculated field can use the values of two or more fields that are already present in the events to perform calculations. A calculated field can use any field as a source, as long as the field is extracted before the calculated field is defined1.
An automatic lookup is a way to enrich events with additional fields from an external source, such as a CSV file or a database. An automatic lookup can add fields to events based on the values of existing fields, such as host, source, sourcetype, or any other extracted field2. An automatic lookup is performed before the calculated fields are defined, so the fields added by the lookup can be used as sources for the calculated fields3.
Therefore, a calculated field can use a field added by an automatic lookup as a source.
Reference:
About calculated fields
About lookups
Search time processing
NEW QUESTION # 104
To identify all of the contributing events within a transaction that contain at least one REJECTevent, which syntax is correct?
- A. index=main | transaction sessionid | search REJECT
- B. index=main | transaction sessionid | where transaction=reject
- C. index=main REJECT | transaction sessionid
- D. index=main | transaction sessionid | where transaction="REJECT*"
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION # 105
Which field will be used to populate the field if the productName and product:d fields have values for a given
event?
| eval productINFO=coalesco(productName,productid)
- A. The value for the productName field because it appears first.
- B. Both field values will be used and the product INFO field will become a multivalue field for the given
event. - C. The value for the field because it appears second.
- D. Neither field value will be used and the field will be assigned a NULL value for the given event.
Answer: A
Explanation:
The correct answer is B. The value for the productName field because it appears first.
The coalesce function is an eval function that takes an arbitrary number of arguments and returns the first
value that is not null.A null value means that the field has no value at all, while an empty value means that the
field has a value, but it is "" or zero-length1.
The coalesce function can be used to combine fields that have different names but represent the same data,
such as IP address or user name.The coalesce function can also be used to rename fields for clarity or
convenience2.
The syntax for the coalesce function is:
coalesce(<field1>,<field2>,...)
The coalesce function will return the value of the first field that is not null in the argument list. If all fields are
null, the coalesce function will return null.
For example, if you have a set of events where the IP address is extracted to either clientip or ipaddress, you
can use the coalesce function to define a new field called ip, that takes the value of either clientip or ipaddress,
depending on which is not null:
| eval ip=coalesce(clientip,ipaddress)
In your example, you have a set of events where the product name is extracted to either productName or
productid, and you use the coalesce function to define a new field called productINFO, that takes the value of
either productName or productid, depending on which is not null:
| eval productINFO=coalesce(productName,productid)
If both productName and productid fields have values for a given event, the coalesce function will return the
value of the productName field because it appears first in the argument list. The productid field will be ignored
by the coalesce function.
Therefore, the value for the productName field will be used to populate the productINFO field if both fields
have values for a given event.
References:
Search Command> Coalesce
USAGE OF SPLUNK EVAL FUNCTION : COALESCE
NEW QUESTION # 106
Which of the following examples would use a POST workflow action?
- A. Perform an external IP lookup based on a domain value found in events.
- B. Launch secondary Splunk searches that use one or more field values from selected events.
- C. Use the field values in an HTTP error event to create a new ticket in an external system.
- D. Open a web browser to look up an HTTP status code.
Answer: C
Explanation:
The correct answer is B. Use the field values in an HTTP error event to create a new ticket in an external
system.
A workflow action is a knowledge object that enables a variety of interactions between fields in events and
other web resources.Workflow actions can create HTML links, generate HTTP POST requests, or launch
secondary searches based on field values1.
There are three types of workflow actions that can be set up using Splunk Web: GET, POST, and Search2.
GET workflow actions create typical HTML links to do things like perform Google searches on specific
values or run domain name queries against external WHOIS databases2.
POST workflow actions generate an HTTP POST request to a specified URI.This action type enables
you to do things like creating entries in external issue management systems using a set of relevant field
values2.
Search workflow actions launch secondary searches that use specific field values from an event, such as
a search that looks for the occurrence of specific combinations of ipaddress and http_status field values
in your index over a specific time range2.
Therefore, the example that would use a POST workflow action is B. Use the field values in an HTTP error
event to create a new ticket in an external system. This example requires sending an HTTP POST request to
the URI of the external system with the field values from the event as arguments.
The other examples would use different types of workflow actions. These examples are:
A: Perform an external IP lookup based on a domain value found in events: This example would use a
GET workflow action to create a link to an external IP lookup service with the domain value as a
parameter.
C: Launch secondary Splunk searches that use one or more field values from selected events: This
example would use a Search workflow action to run another Splunk search with the field values from
the event as search terms.
D: Open a web browser to look up an HTTP status code: This example would also use a GET workflow
action to create a link to a web page that explains the meaning of the HTTP status code.
References:
Splexicon:Workflowaction
About workflow actions in Splunk Web
NEW QUESTION # 107
Which of the following are required to create a POST workflow action?
- A. Label, URI, post arguments.
- B. XMI attributes, URI, name.
- C. URI, search string, time range picker.
- D. Label, URI, search string.
Answer: A
Explanation:
Explanation
POST workflow actions are custom actions that send a POST request to a web server when you click on a field value in your search results. POST workflow actions can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. One of the options that are required to create a POST workflow action is post arguments. Post arguments are key-value pairs that are sent in the body of the POST request to provide additional information to the web server. Post arguments can include field values from your data by using dollar signs around the field names.
NEW QUESTION # 108
Which of the following statements about calculated fields in Splunk is true?
- A. Calculated fields cannot be chained together to create more complex fields
- B. Calculated fields can only be used in saved reports.
- C. Calculated fields can be chained together to create more complex fields.
- D. Calculated fields can only be used in dashboards.
Answer: C
Explanation:
The correct answer is B. Calculated fields can be chained together to create more complex fields.
Calculated fields are fields that are added to events at search time by using eval expressions. They can be used to perform calculations with the values of two or more fields already present in those events. Calculated fields can be defined with Splunk Web or in the props.conf file. They can be used in searches, reports, dashboards, and data models like any other extracted field1.
Calculated fields can also be chained together to create more complex fields. This means that you can use a calculated field as an input for another calculated field. For example, if you have a calculated field named total that sums up the values of two fields named price and tax, you can use the total field to create another calculated field named discount that applies a percentage discount to the total field. To do this, you need to define the discount field with an eval expression that references the total field, such as:
discount = total * 0.9
This will create a new field named discount that is equal to 90% of the total field value for each event2.
References:
* About calculated fields
* Chaining calculated fields
NEW QUESTION # 109
To identify all of the contributing events within a transaction that contain at least one REJECTevent, which syntax is correct?
- A. index=main | transaction sessionid | search REJECT
- B. index=main | transaction sessionid | where transaction=reject
- C. index=main REJECT | transaction sessionid
- D. index=main | transaction sessionid | where transaction="REJECT*"
Answer: A
NEW QUESTION # 110
When would a user select delimited field extractions using the Field Extractor (FX)?
- A. When a log file has values that are separated by the same character, for example, commas.
- B. When a log file contains empty lines or comments.
- C. When the file has a header that might provide information about its structure or format.
- D. With structured files such as JSON or XML.
Answer: A
Explanation:
The correct answer is A. When a log file has values that are separated by the same character, for example, commas.
The Field Extractor (FX) is a utility in Splunk Web that allows you to create new fields from your events by using either regular expressions or delimiters. The FX provides a graphical interface that guides you through the steps of defining and testing your field extractions1.
The FX supports two field extraction methods: regular expression and delimited. The regular expression method works best with unstructured event data, such as logs or messages, that do not have a consistent format or structure. You select a sample event and highlight one or more fields to extract from that event, and the FX generates a regular expression that matches similar events in your data set and extracts the fields from them1.
The delimited method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma, a tab, or a space. You select a sample event, identify the delimiter, and then rename the fields that the FX finds1.
Therefore, you would select the delimited field extraction method when you have a log file that has values that are separated by the same character, for example, commas. This method will allow you to easily extract the fields based on the delimiter without writing complex regular expressions.
The other options are not correct because they are not suitable for the delimited field extraction method. These options are:
* B. When a log file contains empty lines or comments: This option does not indicate that the log file has a structured format or a common delimiter. The delimited method might not work well with this type of data, as it might miss some fields or include some unwanted values.
* C. With structured files such as JSON or XML: This option does not require the delimited method, as Splunk can automatically extract fields from JSON or XML files by using indexed extractions or search-time extractions2. The delimited method might not work well with this type of data, as it might not recognize the nested structure or the special characters.
* D. When the file has a header that might provide information about its structure or format: This option does not indicate that the file has a common delimiter between the fields. The delimited method might not work well with this type of data, as it might not be able to identify the fields based on the header information.
References:
* Build field extractions with the field extractor
* Configure indexed field extraction
NEW QUESTION # 111
When performing a regular expression (regex) field extraction using the Field Extractor (FX), what happens when the requireoption is used?
- A. Only events with the required string will be included in the extraction.
- B. The regex can no longer be edited.
- C. The field being extracted will be required for all future events.
- D. The events without the required field will not display in searches.
Answer: D
NEW QUESTION # 112
Data models are composed of one or more of which of the following datasets? (select all that apply)
- A. Any child of event, transaction, and search datasets
- B. Events datasets
- C. Search datasets
- D. Transaction datasets
Answer: B,C,D
Explanation:
Explanation
Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.
https://docs.splunk.com/Splexicon:Datamodeldataset
NEW QUESTION # 113
Which of the following statements describes the use of the Filed Extractor (FX)?
- A. The Field Extractor automatically extracts all field at search time.
- B. Field extracted using the Extracted persist as knowledge objects.
- C. The Field Extractor uses PERL to extract field from the raw events.
- D. Fields extracted using the Field Extractor do not persist and must be defined for each search.
Answer: B
Explanation:
The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface or
by manually editing the regular expression2. The FX allows you to create field extractions that persist as
knowledge objects, which are entities that you create to add knowledge to your data and make it easier to
search and analyze2. Field extractions are methods that extract fields from your raw data using various
techniques such as regular expressions, delimiters or key-value pairs2. When you create a field extraction
using the FX, you can save it as a knowledge object that applies to your data at search time2. You can also
manage and share your field extractions with other users in your organization2. Therefore, option C is correct,
while options A, B and D are incorrect because they do not describe the use of the FX.
NEW QUESTION # 114
Which of the following searches show a valid use of a macro? (Choose all that apply.) index=main source=mySource oldField=* |'makeMyField(oldField)'| table _time
- A. | table _time newField
- B. newField
index=main source=mySource oldField=* | stats if('makeMyField(oldField)') | - C. table _time newField
index=main source=mySource oldField=* | "'newField('makeMyField(oldField)')'" - D. table _time newField
index=main source=mySource oldField=* | eval newField='makeMyField(oldField)'|
Answer: B,D
Explanation:
Explanation/Reference: https://answers.splunk.com/answers/574643/field-showing-an-additional-and-not-visible-value-1.html
NEW QUESTION # 115
Which of the following can be used with the eval command tostring function (select all that apply)
- A. ''hex''
- B. ''duration''
- C. ''commas''
- D. ''Decimal''
Answer: A,B,C
Explanation:
Explanation
https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/ConversionFunctions#tostring.28X.2CY.
NEW QUESTION # 116
There are several ways to access the field extractor.
Which option automatically identifies the data type, source type, and sample event?
- A. Event Actions > Extract Fields
- B. Settings > Field Extractions > Open Field Extractor
- C. Settings > Field Extractions > New Field Extraction
- D. Fields sidebar > Extract New Fields
Answer: A
Explanation:
Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.0.4/Knowledge/Managesearch- timefieldextractions
NEW QUESTION # 117
Given the macro definition below, what should be entered into the Name and Arguments fileds to correctly
configured the macro?
- A. The macro name issessiontracker(2)and the Arguments are$action$, $JESSIONID$.
- B. The macro name issessiontrackerand the arguments are$action$, $JESSIONID$.
- C. The macro name issessiontracker(2)and the arguments areaction, JESSIONID.
- D. The macro name issessiontrackerand the arguments areaction, JESSIONID.
Answer: C
Explanation:
Reference:https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Definesearchmacros
The macro definition below shows a macro that tracks user sessions based on two arguments: action and
JSESSIONID.
sessiontracker(2)
The macro definition does the following:
It specifies the name of the macro as sessiontracker. This is the name that will be used to execute the macro in
a search string.
It specifies the number of arguments for the macro as 2. This indicates that the macro takes two arguments
when it is executed.
It specifies the code for the macro asindex=main sourcetype=access_combined_wcookie action=$action$
JSESSIONID=$JSESSIONID$ | stats count by JSESSIONID. This is the search string that will be run when
the macro is executed. The search string can contain any partof a search, such as search terms, commands,
arguments, etc. The search string can also include variables for the arguments using dollar signs around them.
In this case, action and JSESSIONID are variables for the arguments that will be replaced by their values when
the macro is executed.
Therefore, to correctly configure the macro, you should enter sessiontracker as the name and action,
JSESSIONID as the arguments. Alternatively, you can use sessiontracker(2) as the name and leave the
arguments blank.
NEW QUESTION # 118
Which of the following statements is true, especially in large environments?
- A. Use the transaction command when you want to see the results of a calculation.
- B. Use the scats command when you next to group events by two or more fields.
- C. The transaction command is faster and more efficient than the stats command.
- D. The stats command is faster and more efficient than the transaction command
Answer: D
Explanation:
Reference:https://answers.splunk.com/answers/103/transaction-vs-stats-commands.html
The stats command is faster and more efficient than the transaction command, especially in large
environments. The stats command is used to calculate summary statistics on the events, such as count, sum,
average, etc. The stats command can group events by one or more fields or by time buckets. The stats
command does not create new events from groups of events, but rather creates new fields with statistical
values. The transaction command is used to group events into transactions based on some common
characteristics, such as fields, time, or both. The transaction command creates new events from groups of
events that share one or more fields. The transaction command also creates some additional fields for each
transaction, such as duration, eventcount, startime, etc. The transaction command is slower and more
resource-intensive than the stats command because it has to process more data and create more events and
fields.
NEW QUESTION # 119
A space is an implied _____ in a search string.
- A. NOT
- B. OR
- C. ()
- D. AND
Answer: D
Explanation:
Explanation
A space is an implied AND in a search string, which means that it acts as a logical operator that returns events that match both terms on either side of the space2. For example, status=200 method=GET will return events that have both status=200 and method=GET2. Therefore, option B is correct, while options A, C and D are incorrect because they are not implied by a space in a search string.
NEW QUESTION # 120
Which of the following statements describes the use of the Field Extractor (FX)?
- A. The Field Extractor uses PERL to extract fields from the raw events.
- B. The Field Extractor automatically extracts all fields at search time.
- C. Fields extracted using the Field Extractor persist as knowledge objects.
- D. Fields extracted using the Field Extractor do not persist and must be defined for each search.
Answer: C
Explanation:
The statement that fields extracted using the Field Extractor persist as knowledge objects is true. The Field
Extractor (FX) is a graphical tool that allows you to extract fields from raw events using regularexpressions or
delimiters. The fields extracted by the FX are saved as knowledge objects that can be used in future searches
or shared with other users.
NEW QUESTION # 121
Which of the following statements describes field aliases?
- A. Field alias names are not case sensitive when used as part of a search.
- B. Field aliases can be used in lookup file definitions.
- C. Field alias names replace the original field name.
- D. Field aliases only normalize data across sources and sourcetypes.
Answer: B
Explanation:
Field aliases are alternative names for fields in Splunk. Field aliases can be used to normalize data across
different sources and sourcetypes that have different field names for the same concept. For example, you can
create a field alias for src_ip that maps to clientip, source_address, or any other field name that represents the
source IP address in different sourcetypes. Field aliases can also be used in lookup file definitions to map
fields in your data to fields in the lookup file. For example, you can use a field alias for src_ip to map it to
ip_address in a lookup file that contains geolocation information for IP addresses. Field alias names do not
replace the original field name, but rather create a copy of the field with a different name. Field alias names
are case sensitive when used as part of a search, meaning that src_ip and SRC_IP are different fields.
NEW QUESTION # 122
Which of the following eval command function is valid?
- A. Print ()
- B. Int ()
- C. Tostring ()
- D. Count ( )
Answer: C
Explanation:
The eval command supports a number of functions that you can use in your expressions to
performcalculations, conversions, string manipulations and more2. One of the eval command functions is
tostring(), which converts a numeric value to a string value2. Therefore, option D is correct, while options A,
B and C are incorrect because they are not valid eval command functions.
NEW QUESTION # 123
Data model fields can be added using the Auto-Extracted method. Which of the following statements describe Auto-Extracted fields? (select all that apply)
- A. Auto-Extracted fields can be hidden in Pivot.
- B. Auto-Extracted fields can be given a friendly name for use in Pivot.
- C. Auto-Extracted fields can be added if they already exist in the dataset with constraints.
- D. Auto-Extracted fields can have their data type changed.
Answer: A,B,C,D
Explanation:
Data model fields are fields that describe the attributes of a dataset in a data model2. Data model fields can be added using various methods such as Auto-Extracted, Evaluated or Lookup2. Auto-Extracted fields are fields that are automatically extracted from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. Auto-Extracted fields can be hidden in Pivot, which means that you can choose whether to display them or not in the Pivot interface2. Therefore, option A is correct. Auto-Extracted fields can have their data type changed, which means that you can specify whether they are strings, numbers, booleans or timestamps2. Therefore, option B is correct. Auto-Extracted fields can be given a friendly name for use in Pivot, which means that you can assign an alternative name to them that is more descriptive or user-friendly than the original field name2. Therefore, option C is correct. Auto-Extracted fields can be added if they already exist in the dataset with constraints, which means that you can include them in your data model even if they are already extracted from your raw data by applying filters or constraints to limit the scope of your dataset2. Therefore, option D is correct.
NEW QUESTION # 124
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